NLoS target localization in IRS-assisted FDA-MIMO radar: A tensor decomposition perspective

Abstract

Intelligent reconfigurable surface (IRS) provides an innovative solution for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar systems in the localization of non-line-of-sight (NLoS) traffic targets. In this paper, we consider an IRS-assisted FDA-MIMO radar system and propose a NLoS multi-target localization algorithm based on tensor decomposition. Specifically, the received signals are first constructed as a third-order tensor model. Then, a sequential minimum description length (MDL) method is employed to estimate the number of targets in advance. With tensor decomposition, the steering matrices containing angle and range information are obtained. In the estimated transmitting steering matrix, the directions-of-departure (DODs) and ranges are successfully decoupled after solving the phase ambiguity. In the estimated receiving steering matrix, a two-dimensional grid search method is applied to obtain the horizontal directions-of-arrival (DOAs) and vertical DOAs. Finally, the localization of NLoS targets is determined by utilizing the geometric relationships of these estimated parameters. Besides, the Cramér-Rao bound (CRB) for the estimations of angle and range is derived as a performance benchmark. Simulation results demonstrate the effectiveness of the proposed algorithm in locating NLoS targets.